Calibration of Satellite Imagery with Multispectral UAV Imagery

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown a tremendous potential normalized difference vegetation index (NDVI) for precision agriculture. In this study, data captured from a UAV equipped with a Multispectral Mica Sense Red Edge camera used as ground-truth information to calibrate Sentinel-2 imagery. UAV-based NDVI allowed crop estimation at 10-cm pixel resolution by discriminating no-green vegetation pixels. The reflectance value and NDVI of the crops at different stages were derived from both UAV and Sentinel-2 images. The UAV Multispectral mapping method used in this study provided advanced information about the physical conditions of the study area (Roorkee) and improved land feature delineation. The result shows that UAV data produced more accurate reflectance values than Sentinel-2 imagery. However, the accuracy of the vegetation index is not wholly dependent on the accuracy of the reflectance. The UAV-derived NDVI has relatively low sensitivity to the vegetation coverage and insignificantly affected by environmental factors compared to NDVI derived from Sentinel-2 image.